Explainable AI for Medical Diagnosis: A Review of Current Techniques

Abstract

The application of AI systems in medicine, especially in medical diagnosis, indicates the necessity of interpret-ability and transparency. Deep learning methods have achieved unprecedented success in identifying complex patterns and improving diagnostic accuracy, but their "black-box" status has the tendency to limit their concrete utility in high-stakes clinical environments. Explainable AI (XAI) systems have been envisioned as a solution to this, offering interpretability mechanisms to make algorithmic decision-making explainable to clinicians. The systems utilize an array of explanation methods visual, text, local, and global to render diagnostic conclusions intelligible, thus supporting clinical trust, accountability, and informed decision-making. Besides technical performance, the realities of incorporating XAI systems into standard clinical practice workflows, meeting future regulatory requirements, and enabling human-AI collaboration are also issues that need to be resolved. Of more significance, XAI not only plays a part in the verification of existing disease indicators but also in the identification of novel biomarkers that can enhance diagnostic procedures. These operational and systemic hurdles must be overcome if explainable AI is to be widely and seamlessly implemented across the healthcare system. As research progresses, the development of stable, user-centered, and regulation-compliant XAI systems will be instrumental in realizing the full potential of AI to improve patient care.